{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas 的两大主要工具DataFrame和Series的框架设计和API设计方向，都是来源于统计分析R语言\n",
    "# 对于大数据处理更闻名的是Hadhoop 和 Spark,它们的优势是基于集群的云端数据处理， 如果你的数据有几个GB，甚至1-2TB，\n",
    "# 那么使用pandas 也是处理数据的最好选择\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_day_change= np.load('./stock_day_change.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.281206</td>\n",
       "      <td>1.086986</td>\n",
       "      <td>-0.167054</td>\n",
       "      <td>-2.314920</td>\n",
       "      <td>-1.497563</td>\n",
       "      <td>-0.408868</td>\n",
       "      <td>0.253975</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.463962</td>\n",
       "      <td>-0.001128</td>\n",
       "      <td>0.592206</td>\n",
       "      <td>0.550152</td>\n",
       "      <td>2.073070</td>\n",
       "      <td>-0.274976</td>\n",
       "      <td>0.821165</td>\n",
       "      <td>0.555181</td>\n",
       "      <td>-0.378557</td>\n",
       "      <td>-0.669200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.944059</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "<p>5 rows × 504 columns</p>\n",
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       "        0         1         2         3         4         5         6    \\\n",
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       "1 -0.958488  1.000000  0.432855 -0.759380  0.029757  0.205954  2.064302   \n",
       "2 -0.542592 -1.320282 -0.373606 -0.281206  1.086986 -0.167054 -2.314920   \n",
       "3 -1.176977 -0.944059  2.183147  1.996997  0.170396 -1.469498  0.018698   \n",
       "4  0.281298  0.736610 -0.465623 -0.901330 -0.595694 -0.287408 -1.244728   \n",
       "\n",
       "        7         8         9    ...       494       495       496       497  \\\n",
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       "2 -1.497563 -0.408868  0.253975  ... -0.463962 -0.001128  0.592206  0.550152   \n",
       "3  0.531658  0.306343  0.268836  ...  0.833607 -0.272171  0.912249  1.677577   \n",
       "4 -0.159261 -0.372950  0.849160  ...  1.411843 -1.091312  1.717159  0.930921   \n",
       "\n",
       "        498       499       500       501       502       503  \n",
       "0 -0.357714 -0.715130 -0.488980  0.235451 -0.060989  1.535490  \n",
       "1  0.771722  0.928030  0.319106  0.429026 -0.723847 -0.570307  \n",
       "2  2.073070 -0.274976  0.821165  0.555181 -0.378557 -0.669200  \n",
       "3 -0.923915  1.140203  0.038951  0.961553  0.867025 -0.562292  \n",
       "4 -1.846374 -0.389850  0.923967  1.067129 -0.138686 -0.151059  \n",
       "\n",
       "[5 rows x 504 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# panda 是在Numpy的基础上构建的\n",
    "pd.DataFrame(stock_day_change).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_symbols = ['股票'+str(i) for i in range(stock_day_change.shape[0])]\n",
    "days = pd.date_range('2019-01-01', periods=stock_day_change.shape[1], freq='1d')\n",
    "df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票0</th>\n",
       "      <th>股票1</th>\n",
       "      <th>股票2</th>\n",
       "      <th>股票3</th>\n",
       "      <th>股票4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.633936</td>\n",
       "      <td>-0.958488</td>\n",
       "      <td>-0.542592</td>\n",
       "      <td>-1.176977</td>\n",
       "      <td>0.281298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>-0.919088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.320282</td>\n",
       "      <td>-0.944059</td>\n",
       "      <td>0.736610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-1.063508</td>\n",
       "      <td>0.432855</td>\n",
       "      <td>-0.373606</td>\n",
       "      <td>2.183147</td>\n",
       "      <td>-0.465623</td>\n",
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      ],
      "text/plain": [
       "                 股票0       股票1       股票2       股票3       股票4\n",
       "2019-01-01 -0.633936 -0.958488 -0.542592 -1.176977  0.281298\n",
       "2019-01-02 -0.919088  1.000000 -1.320282 -0.944059  0.736610\n",
       "2019-01-03 -1.063508  0.432855 -0.373606  2.183147 -0.465623"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  df.T\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票0</th>\n",
       "      <th>股票1</th>\n",
       "      <th>股票2</th>\n",
       "      <th>股票3</th>\n",
       "      <th>股票4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.086731</td>\n",
       "      <td>0.150944</td>\n",
       "      <td>-0.331624</td>\n",
       "      <td>0.470044</td>\n",
       "      <td>0.005435</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-22</th>\n",
       "      <td>-0.151713</td>\n",
       "      <td>0.461468</td>\n",
       "      <td>-0.093144</td>\n",
       "      <td>0.330739</td>\n",
       "      <td>0.139496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-12</th>\n",
       "      <td>-0.305601</td>\n",
       "      <td>-0.238774</td>\n",
       "      <td>0.053779</td>\n",
       "      <td>0.115701</td>\n",
       "      <td>-0.254766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>-0.151442</td>\n",
       "      <td>-0.026707</td>\n",
       "      <td>-0.144114</td>\n",
       "      <td>0.249801</td>\n",
       "      <td>-0.119242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>0.224956</td>\n",
       "      <td>0.147124</td>\n",
       "      <td>-0.125639</td>\n",
       "      <td>-0.094054</td>\n",
       "      <td>0.146188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 股票0       股票1       股票2       股票3       股票4\n",
       "2019-01-01  0.086731  0.150944 -0.331624  0.470044  0.005435\n",
       "2019-01-22 -0.151713  0.461468 -0.093144  0.330739  0.139496\n",
       "2019-02-12 -0.305601 -0.238774  0.053779  0.115701 -0.254766\n",
       "2019-03-05 -0.151442 -0.026707 -0.144114  0.249801 -0.119242\n",
       "2019-03-26  0.224956  0.147124 -0.125639 -0.094054  0.146188"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据重新采样 周期为21\n",
    "df_20 = df.resample('21D').mean()\n",
    "df_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118434a90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 本质上 pandas 在基于封装了Numpy上还封装了Matplotlib, 起到承上启下的重要作用\n",
    "df_stock0 = df['股票0']\n",
    "df_stock0.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1188d2358>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对于日线的重采样整理 \n",
    "# 以5天为周期的重采样\n",
    "df_stock0_5 = df_stock0.cumsum().resample('5D').ohlc()\n",
    "df_stock0_5.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118958320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gahto9i5UbAr5xPlfQgiydYKMbB2Z938ysrVkZuswdO2Fq/seCJgKrs2hnFfRn4skPSezjs/icuzlIu2zuk11xjccX6B9N2zYwNmzZzl37hzR0dE0aNAAX19ffH19OXDgAJ06deLOnTuEh4cDcPDgQXr16lWk8ebmpU/yapWaj175nO8P9mDIjnMk79mDRQFWbHGzM2P0K1UYtasDJ8rsx+jPD2HQtkIn3lzdu6Qfb//7N/3NVc/u0GwMONTMcXchBAv3BrPnUuSDJJ6RrSNTqyMjS6t/zdaRV5WKya+8x5A7p+H3oTD8LzB6tg9lSNLL6uDBg/Tu3Ru1Wo2DgwN+fn6cOHECHx8f5s2bx8WLF/Hw8CAuLo7w8HCOHDlSbIuE/NdLn+RB/4lcvs9Awk4sRzdrOh5+figFKMw/3Lcym8/d5euU3oy/tRgu/K5Pxk/j1hF9cr/6JxiaQoOh+hLHVnnfFF5xOIS5u69Sx8UKFxtTjAxUGBmo0Bio0Tz4swojtQqNof7V6KFtf14I5/O9EXi0mkaTQ0Nh18fQYe7TnYsklRAFveIuLrnVACtfvjxxcXHs2LEDX19fYmNjWbduHebm5lhYWDyT2EpFkgd4u+5IJrfbwpBfIohau5qy/Qbk28bIQMWMLp70XJJIf5t9OO36GKq9CppC/sfR6fRJ/dB80s+fIvK8LcKsNio7V5S7Kah2z0cxNkZlrEHRGKMYa1D982pszNW4TPYeDuUtV3ve71ofI8fCl0NuU9OB6KTjDAxUsd97GI4nl0GVVlC9faH7kiTpUb6+vixdupSBAwcSGxvL/v37+eqrrwBo0qQJ8+bNY+/evcTExNC9e3e6d3/Ki8VCKDVJ3sTAhK5vzuBi4FCqLJiHbeeuqM3N821X39WG3o1ceedEbzYYTYX9X0HrAj4Nm50B59fB4QUQfRWtSQXCTldFl62gca6INjUdERuPLiMdkZ6BSE9Hl6F/fVgZYCLACbj55zKcZs3EolWrQp2/xkDN0v716Lr4MB0vtuSg/RE0m94Bp7pQplyh+pIk6VFdunThyJEjeHt7oygKX375JY6OjgD4+Piwa9cuqlSpQsWKFYmNjcXHx+eZxfbSlBouqDkrR/DajL9QBvWk+oS8V5D6R0JaFv5zA5muLKZ1diDKiMNgXy33BukJcPJHOLoYkiPA0RPRdAx3vj9A0p69VPx5JaZ16+baXAiByMwkIS6ZN5ceICMllSU9a2FPJpFfzCQ9KAjbYcOwHzMaxaBwn9Mh0Sl0WXQIT+N7rMgah+LcAPpvlNMqpRfOi1JquKiV2lLDBfVmz+kcr2VE5qrfyIgIL1AbSxNDPulYk4mJ3chUafRrwub04ZgYDrun6KdBBkwFe3fo/we8dYC4C1kk7dqN/btj8kzwoJ/dozUwZNTWYIIyjfjsf21xqefJH2ZXCJrWB6uePYlZtozbQ4aSHR2dZ1//5WpnxrIB9TmaaMt3JsPgZiAc+aZQfUiS9OIodUnextiGsmPfA53g5OfvFbjda56OeFevyleZ3eHGPri89d+NUVdh0zsw30v/EFMVf/3slYGbofIrpF28yL0vZmLm64PtkCEFOt60rRc5GBzNjC6e1HaxYPKhycw8PpOPT3xG5DtdKDdjBmlnz3KzazdST58p1N9BfVcbZvfw5ot7DTln7oPY8xncPVuoPiRJejGUuiQP0LbpAM75lcdqz1nunD1coDaKovDZ6zVZSxtCDd0QOybqa8ms7gPfNtBPhaw7AEadgh4/glMdALTJydwZ+x5qGxucZs1CKcCwyC9Hb7HiyC2G+bjRzsuakQEj2XR9E0M9h+Jo5siEAxNQd/DHdc1qFGNjbg0YQOzKn/NdNPhhnbydGPdqdQZG9yPZwBp+HwKZKQVuL0nSi6FUJnlFUfD7aAGpxhD0yTh0BVhBCsDZ2pQxrWvwfnI/lIRQWNERbh8Gv/EwNgjazwGbSg/2F0IQPnkyWXfuUH7uHAysrfM9xuHgaKZuDqKluz1v+tkwaMcgjkcc57OmnzGm7hhm+swkIiWC6cemY1yjBm7rf8Pcx4fIGTO4+/776FIKnqj/16IyberXYHjyMETMddiRfxE3SZJeLKUyyQO4OHsQ3bsVFS7Hsn9ED0RWVoHaDWrqSqpTI75SDSHN/wt49wK0/AjM7B7bN37NGpL+3IH9mDGY1quXb98h0SmMWHWaSnZmvPtaGQbu6E9oUijftvqWLlW7AFC7bG3e8n6LbTe2seX6FtRlyuD87TfYv/ceiTt2crPnG2Rcv16gc/nnyV5VJV+WajvC6ZVwcVOB2kqS9GIotUkeoM24+ZzpVB2HwIucerMHutTUfNsYqFXM7OrF4rRW9D7nzamIzBz3S794kcgvZmLm44Pt0PzH4RPTsxiy4gSKAqPbK4zYMxitTstPbX+iWflmj+w7zHMYdcvWZfqx6YQmhaKoVNgNH4bLD8vRxsUR0qMniX/+WaC/A0O1ikV967HJaiAXqIx20yhIuFOgtpIklXylOsmrVWp6fLGOXW9UxuTUFYL6dCc7NjbfdrXKWzK3Z23uxKfRbfERhq44yZWIpAfbtcnJhI0di9raGqdZM/Mdh9fqBKN+PcOtmFQGtY5jyvEx2Jva88trv1DD9vEpYgYqA77w+QIVKiYcmECWTv8txKxxY9z+2ICmWjXujH2PyC++KNA3FEsTQ757symT1e+SkZFB5vphcjFzSXpCn3zyCbNnz37eYTxQqpM8gKHakMGTVvPrgArogm9y7Y3uZIaF5duuc53yBI5rwbhX3Tl2I4a28/fz/rpzhMamEDFlCllhdyg/ZzYGNjb59jVj+yUCr96jXfOLLL86jdr2tVnZbiVO5k65tnEyd2Jyk8mcjzrP0nNL/z0fBwcqrlyBdf/+xK5Yya2Bg8iKjMw3hgo2pnwyqBPTdIMwCj1E1oF5+baRJKnkK/VJHsDCyILRo3/mm0F2pERHcOONN0i/dCnfdqZGBoxsWYX9H7ZkmE8ltpy/y5cjZ5K4/U/M3x6Jaf0cn014xNoTt1l+MBjv2nv5K2oF7VzbsbT1Uiw1lvm2befWjk6VO7Hs72Wcijz14H3FyAjHSR/hNGc26Zcvc7NrN1KOHsu3P+8KVvj2eJdt2kao9k1HF3oq3zaSVNqtXLkSLy8vvL296d+//yPbzp49S+PGjfHy8qJLly7ExcUBsGDBAjw8PPDy8npQjTIlJYXBgwfToEED6tSpw6ZNRXN/rNQ98ZqX4Lhgxq/sywe/pmKdbUSFb7/FrHHjArcPPX6WhMEDOGtTiVkt3mKobxWG+rhhpsn5qdRjN2Lo98MBylZeT6LqHINrDWZM3TGolIJ/9qZkpdBjSw+yddms77SeMkZlHtmece0aYaPHkHnrFq5r12Di6Zlvnyv3nqVVYFdMjE2wee9o4Wv1SNIz8PCTnxEzZpBxqWhLDWtqVMfxo4/y3CcoKIiuXbty6NAh7OzsiI2NZcGCBZibm/PBBx/g5eXFwoUL8fPzY8qUKSQmJjJv3jycnJy4efMmGo2G+Ph4rKys+Oijj/Dw8KBfv37Ex8fTsGFDzpw5g5mZWa7n/Y9ie+JVUZQeiqIEKYqiUxSl/n+2TVQUJVhRlCuKorz6NMd5VqpYV2HCG98weYAB9ywEt4cOI3H79gK11SankDl5Ahobaxp9v4BmVcvydcBV/L7ax4rDIWRmPzpNMzQ2lbdXB2Luuoxk1d9MajSJsfXGFirBA5gZmjHLZxZRqVF8duSzx+bKa6pW1c+nNzAgYcuWAvXZv6U3f1b9DKv0O1xfObJQ8UhSabJ37166d++OnZ1+dp3NQ8OzCQkJxMfH4+fnB8DAgQPZv1+/TrOXlxd9+/bll19+weB+aZJdu3Yxc+ZMateuTYsWLUhPT+f27dtPHePTFii7AHQFlj78pqIoHkAvoCbgBAQoilJNCFHi7+Y1cGzA+6/N4EP1OGZsLQPvvU92dDQ2A3KvWimEIGLqVDJDQ6m44idMq7mwpJoLZ27HMWvHZaZuDuL7gzd4v7U7nbydSM3SMujnbWQ7fIuRJpm5fvNo6dLyiWP2tPdkZJ2RzD89n+blm9O5SudHtqstLTFr1oykgAAcJk4s0KIpg3r3Ydv8Q3S88ytBu36kZps3nzg+SSpu+V1xFxchRL7/nnKybds29u/fz+bNm/n8888JCgpCCMHvv/+Ou7t7kcb4VFfyQohLQogrOWx6HVgjhMgQQtwEgoFcFiYtedq5tWOEzzg+6JxIeH0XImd8wb05c3J9ojT+t99I3LYN+9GjMW3Q4MH7dVysWT2sMSsGN8RCY8i7a8/y2oID9Fm5hkjz2ZibaPmx7Q9PleD/8WbNN6nvUJ8Zx2ZwO/HxT38Lf3+y74aTfvFigfozUKto+fZcLqurUe7wFLQZ+U8vlaTSplWrVqxbt46YmBgAYh+anWdpaYm1tTUHDhwA4Oeff8bPzw+dTkdoaCgtW7bkyy+/JD4+nuTkZF599VUWLlz4IM+cOVO4ciW5Ka4br+WB0Id+D7v/3mMURRmuKMpJRVFORkVFFVM4hTfAYwA9vfrxbqs73GtTh5hl3xM+YeJjUxLTL18mctp0zJo1w3b4sMf6URQFv2r2bB3VnC96uBFjtIkbBnOwNrFkXcdf8bIvmmX41Co1X/h8gaHKkPH7xz+YVvkP81dagkpFUkBAgfs0NzUhtukkbEjk1p6l+TeQpFKmZs2aTJo0CT8/P7y9vXnvvUfrYa1YsYJx48bh5eXF2bNnmTJlClqtln79+uHp6UmdOnUYO3YsVlZWTJ48maysLLy8vKhVqxaTJ08ukhjzvfGqKEoA4JjDpklCiE339/kL+EAIcfL+798CR4QQv9z/fTmwXQjxe17Het43Xv9Lq9Mybv84AkJ2s/RuG6xWbseseXOc589DZWaGNjmFkO7d0aWk4LbxDwxsbXPsJzw5nJ+CfmLDtQ1kaDNo5NCCmX5TsTXJef+nsStkF+8Hvs9Qz6GMqTvmkW23BgxEGxdLpQKOzQOkZmRxZUZTXI0SsZ5wAdT5r6glSc+CLDX8r6e68SqE8BdC1MrhJ6/5PWFAhYd+dwbuFiD+EkWtUjOj+QxqO9RhZIV9ZHwwhJTDh7k16E2yY2OJ+OQTMm/fxmnO7BwT/I2EG3x88GNe2/Aa666s41XXV9nYeSPL2i4olgQP0Ma1DV2rdmX538s5EXHikW0W/v5kXAsmMySkwP2Zagw54TwI66wIss/9VsTRSpJU3IpruGYz0EtRFI2iKG5AVeB4MR2rWBkbGLPwlYU4WzjzP7MNGMycRMbVq9xo34HErVuxHz0Ks4aP3m4Iig5i7L6xdN7YmZ0hO3mj+hts77qdac2nUcmyUi5HKjrjG4ynYpmKTDgwgYSMhAfvW/jrV5MqzJANQKWmXbmsq0B64Fz9UoaSJL0wnnYKZRdFUcKAJsA2RVF2AgghgoB1wEVgBzDyRZhZkxtLjSWL/RejUWsYmfkTFkv0yc7Mxwfb4cMB/V324+HHGb5rOL229eJY+DGGeg5lZ/edTGg4gXLmz26JPVNDU2b6ziQ2PZZPDn/y4EaOoZMTxjVrkrS7cEne192BFarXMU+4Bld3FEfIkiQVk6edXfOHEMJZCKERQjgIIV59aNt0IURlIYS7EKJg1bJKsPLm5VnUahEJGQmMiVqE487NVFi8CKHA3tt76be9H0N2DeFq3FXG1hvLru67GF13NDbG+Zc1KA41bWsyus5oAm4HsOHahgfvW7T2J+3cObIi7xW4LyMDFdTsRpiwR3dgTs6rYknSc1CSHuZ8Fp7kfGVZg0KoYVuDuS3mcj3+Ou8fn8TWW3/SbXM3xuwbQ0x6DB83+pid3XcyuNZgzI3yXyS8uA2sOZBG5Rox68QsbibcBPTj8gDJe/cUqq8OdVxYkt0B1Z2TEHKwyGOVpMIyNjYmJiam1CR6IQQxMTEYGxsXqp0sa/AENgZvZPIh/fSmKlZVGOI5hLaubTFQPe2zZUUvMiWSblu64WTmxKrXVmGgMuBG23YYli+Pyw/LC9yPVifwnb6dPxlJGdc6+rVrJek5ysrKIiwsjPT09OcdyjNjbGyMs7MzhoaPznLLa3ZNyctKL4DOVTpjakhsX3AAACAASURBVGCKRq3Bx9mn0KUIniUHMwc+bfIp7/71Lhuvb6RHtR5YtPYn5qcVaBMSUFvmXwgNQK1SaO3tyncn2vLB9dX6NWGdahdz9JKUO0NDQ9zc3J53GCVeyc1OJVwb1zb4VfAr0Qn+H6+4vIK7tTtrLq9BCKEfssnOJjkwsFD9dPR2YkVWK7IMzOHg18UUrSRJRankZyjpqSmKQq/qvbgad5XT905j7OWFgb19oWfZ1HWxooyVLTtNO+iXCYwOLqaIJUkqKjLJlxKvub2GhZEFay6vQVGpMPdvRfLBg+gKMZ6pKAodvZ34LNoPYaCBQ3JhEUkq6WSSLyVMDU3pXKUzAbcCiEqNwsLfH5GWRsqhQ4Xqp5O3E/d0llx1eh3OrZHrwUpSCSeTfCnSy70X2SKb9VfXY9awIaoyZQo9ZFOjnAWV7c1YmNYOhA6OLiqmaCVJKgoyyZciLmVcaFa+Gb9d/Y1sNZi38CN53z5EdnaB+1AUhU7e5dkWZkSae2c4+SOk5r/4uSRJz4dM8qVMn+p9iEqLYs/tPVj4+6NNSCC1kM8mdPQuhxCw3bIXZKXA8e+KKVpJkp6WTPKlTDOnZpQ3L8/qS6sxb94cRaMp9JBNJXtzapUvw8rrplCtHRxbAhnJxRSxJElPQyb5UkatUvOG+xucvnea4IwwzJo3JykgAFHI6pKdvJ04F5ZAuOcISIuD0yuKKWJJkp6GTPKlUJcqXdCoNay5vEa/LGBkJOkXLhSqjw5eTgCsv+cEFZvD4W8gO7M4wpUk6SnIJF8KWRlb0c6tHVtvbIXm9UCtLvSQjZOVCQ1crdly/i40HwtJd+H82mKKWJKkJyWTfCnVu3pv0rLT2BoViGmDBoVeSAT0QzZXI5O5bN4AHL30D0fpXthlAyTppSSTfCnlYeuBl70Xa66swdz/FTJv3iTj+vVC9dHOsxxqlcKW8+H6q/mYYLhU8PVjJUkqfjLJl2K9q/fmVuItrtayBij0kI2duYamlW3Zci4cUaMT2FSGg3PloiKSVILIJF+KtanYBhtjG36N2Ymxp+cTD9ncjk3l7J0kaDYGws/BjX3FEK0kSU9CJvlSzEhtRLeq3QgMC0T4NiD9wgWywsML1Uebmo4YqVVsORcO3r3AohwcmFtMEUuSVFgyyZdyPd17oigKAa76h5mSAgq3LKCliSEt3O3Zev4uWpURNBkJIQcgrOSv8CVJpYFM8qWco5kjLSu05OeUvRhWcnuyIZvaTtxLyuDYzRioNwiMreTVvCSVEDLJS/Su3pv4jHgi6lUk9eRJsuPiCtW+VXUHTI3U+iEbjQU0eguubIN7l4opYkmSCkomeYmGjg2pZFmJDeXugFZL8r6/CtXexEhNaw8H/rwQTma2Dhq+BYamcGh+8QQsSVKBySQvoSgKb7i/wW7TG4iytk88yyY+NYtDwdFgZgt1B8L5dRB3qxgiliSpoGSSlwDoVLkTpoZmXKlpScqhQ+hSUwvV3qeqPZYmhmw+d1f/RtN3QKWGA7OLIVpJkgpKJnkJAHMjczpW7sjvTmGIjAySDxwsVHsjAxXtajmyKyiCtEwtWDpD/SFw5heIulpMUUuSlB+Z5KUHelfvzd/OWrLMjZ94yCYlU8u+K/f0b/i8rx+b3/tZEUcqSVJBySQvPVDZqjL1nRpxuqqK5L/+QmQWrnRwo0q22Fto2Hz2/pCNuT00HaWvZxN2qhgiliQpPzLJS4/oXb03gZXS0SUlkXL8RKHaqlUK7T3LsffKPRLTs/RvNhkJpnYQMFXWtJGk50AmeekRLSq0IKKmI1lGKpICdhe6fafaTmRm69gdFKl/Q2MBvuP0T8FeL9zTtJIkPT2Z5KVHGKgM6FyzJ6fdBPEBuwu9LGCdClY4W5v8O8sGoP6bYOUCAZ9CIfuTJOnpyCQvPaZbtW6cdDeA6FjSzp0rVFtFUejo7cTB4GhiU+6P6RtooOXHEHEegjYUQ8SSJOXmqZK8oihfKYpyWVGU84qi/KEoitVD2yYqihKsKMoVRVFeffpQpWfFzsQO65atyFZB7K4/C92+o5cTWp1g+98PVbT07A5la8LeaXItWEl6hp72Sn43UEsI4QVcBSYCKIriAfQCagJtgUWKoqif8ljSM9St3kCCXBSid2xDFPKGaY1yFlQpa/7okI1KDf5TIe4mnFlZxNFKkpSbp0ryQohdQojs+78eBZzv//l1YI0QIkMIcRMIBho+zbGkZ8vb3pvQOuXQhMeScfVaodoqikInbydOhMQSnpD274aqbcClKQR+CZkpRRyxJEk5Kcox+cHAP9/tywOhD20Lu/+e9IJQFIXqrw8A4PKmFYVu39HbCSFgxeGHatcoCvh/AsmRcHRR0QQqSVKe8k3yiqIEKIpyIYef1x/aZxKQDaz6560cusrxO7+iKMMVRTmpKMrJqKioJzkHqZj41+/JdWc1GRu3k3n3bv4NHuJmZ0bXuuVZfvAGN6KS/93g0gjcX4NDCyA1togjliTpv/JN8kIIfyFErRx+NgEoijIQ6AD0Ff8O3oYBFR7qxhnIMUsIIb4TQtQXQtS3t7d/urORipSJgQnJw7thkJLOpe6dSb9SuBo0E9pVR2Og5tMtFx8d1281BTKT4cCcIo5YkqT/etrZNW2B8UAnIcTDZQs3A70URdEoiuIGVAWOP82xpOejZ4+p7BjXnKSsJK73foOUo0cL3LashTHv+lcl8GoUAZfuPbShBnj3huPLID409w4kSXpqTzsm/w1gAexWFOWsoihLAIQQQcA64CKwAxgphNA+5bGk50ClqHi/10JWjanFXbNMbg0dSsLWbQVuP7CpK1XLmvPZ1iDSsx76X6DFBEBA4MyiD1qSpAeednZNFSFEBSFE7fs/bz+0bboQorIQwl0IUfjJ1lKJYWxgzLRuS1j0lhPXnFXc/eADYpYvL9DUSkO1ik871SQ0No2lgTf+3WDlAg2Gwdlf4d7lYoxekko3+cSrVCC2JrbM7rCEOX3MOO9lwb2vZhM5fQZCm/8XtKZV7GjvVY5FfwUTGvvQqJ7P+2BoBns/L8bIJal0k0leKrDKVpX5svXXzGqfyamW5Yn75RfuvDsWXXp6vm0nvVYDlaIwbdvFf980s4Vmo+HyVggtXMVLSZIKRiZ5qVAal2vM5KZTmdU4kjO965AUEMDtwUPQxsfn2c7JyoR3XqnCzqBIAq8+NFW28f/ArCwEfCJLEUtSMZBJXiq0LlW7MNRzKF+4/s21sZ1I//tvQvr0JTPsTp7thvq44Wpryqebg8jMvl+NUmMOfh/CrYMQXPjVqCRJyptM8tITGVVnFK+6vsokzTYivxhBdnQ0Ib17kX7pUq5tNAZqpnaqyY3oFJYfvPnvhroDwdpVliKWpGIgk7z0RFSKimnNpuFl78X78cvJ/GYqioEht/r2I/nQoVzbtXQvi38NBxbuvfZvXRsDI30p4si/4cLvz+gMJKkESb4HOyfB+d8gJbpIu5ZJXnpixgbGLGi5ADsTO965OQvjH77GsEIFQt96m/iNG3NtN6WDB9k6wYztD02drNUNHDxhnyxFLJVC296DI9/AhqHwVRX4roW+LPetI6DNzrd5XmSSl56KrYkti1otIkuXxahzU7H5YRGmDeoTPmEi0UuW5jiX3sXWlLf9KrPl3F2OXI/Rv6lS3S9FHAKnC18QTZJeWJe26he7f2UyDN0LLSaC2khf9uPHtvClG6ztByd/hPjbhe5eKWyt8OJUv359cfLkyecdhvQEjoUf4+3db1PfsT7f+s4navInJG7Zgknduti9/RZmPj4oyr9169IytfjPDcRcY8DW0c0xVKv0s2t+6gDRV2D0Wf1NWUl6maUnwreNwNQGhv8FasN/t6XFwY1A/drIwXsg8f7EBrtqULkVVPEH12ZgaIKiKKeEEPVzOoS8kpeKRKNyjZjSZApHw48y4/SXlJv5BY5Tp5AVHk7o8LcI6dadxJ27HqwZa2KkZkpHD65EJvHzkfvliP8pRZwSpf/q+ixpsyEj6dkeU5L2fg5J4dBx/qMJHsDEGmp2hk4LYWwQ/O8YvDoDLJ3h5A+wqhvZn7gSO7pFnoeQV/JSkVpwegHL/l7G2HpjGVxrMCIzk4QtW4j+7juybt3GqHJl7IYPo0z79qBWM/DHE5y5FcfeD1pgb6HRd7Kmr/4BKZvKUKWV/qrFtXnRX9nHheivkK7vhZv7ISsV2s2CBkOL9jiSlJPQE7C8NTQcDq99WeBmuowMknfvJGHdzySfDAKdwOPK5Vyv5GWSl4qUTugYv388O0J2MMdvDm1c2wAgtFoSd+wgZul3ZFy9iqGzM7ZDhxLj05p2i47yeu3yzO7hre8kPVFf0+b6Hrh5ALLTQGUIFZvc/5raChxq6a/8CyMjGUIO6JN68B6Iva5/37ICVH5F/3U4OADqD4Z2Xz5+ZSVJRUWbBUt9IT0BRh4DjUWeuwudjrRTp0jYvJnEHTvRJSVhULYsZTp0wPL1TphUry6TvPTsZGgzGLpzKJdiL7HYfzENHBs82CZ0OpL/+ovoJUtJP38eg7JlOdOkPZO1Vfh1VEvqVbR+tLOsdLh95P645F64F6R/39xBn5ir+EOllvoSCf+l00HEeX3b6/vg9lHQZYGhKbj63G/fCmyr6D8wdFrY8xkcmgcVm0PPlTn3K0lP68Bc2PMp9FoN1V/LdbeMmzf1iX3zFrLu3EExNaVMa3/KdOqEWePGKGr90tl5jcnLJC8Vi9j0WN7c8SahSaHM8JlBW9e2j2wXQpB65AjRS5aSevw4iRpzDtVuxTvzJ2JkZZl7x4nh96/EA+DGPv3NKRRwqg2VW5Hq3Azj1AhUN/bqE3vq/TnHjp76pF65Fbg0BgNN7sc4vw42vQMWDvp/hI61nv4vRJL+EXMdFjfVr3n8xs+Pbc6OiyNx23YSNm8m/fx5UKkwa9IEy9c7YeHvj8rU9JH9j9+MpVElW5nkpWcvISOBUXtHcfbeWcY1GEd/j/457pd6+gwXv5qP2ZljaE1MKTugPzYDB2BgY5P3AXRaxN0zRJ3dTvbVABwS/0aN/sZuhsYWI/dWKJVb6a/0LRwKF/ydU/p7A+mJ0HUp1OhYuPaSlBMhYOXrcPcMjDwOZcrp39bpSAoIIGHjJpL374fsbDTu7lh26kSZDh0wdCibY3d34tPouPAgZ6a0kUleej7Ss9OZeGAiAbcD6O/Rnw/qf4BKeXxSlxCCd6etpfb+TTQOO4fKxATbt9/GZuAAVJpHr7rTs7QcvRFDwKVI9l66x92EdBQFmjoZ0NUulJ23VeyOs6deRVsmtKtOfdd8PixykxgOa/vqE37LSeA7rvD3ASTpYWdXw8a3of1caDDkwduRX35F7A8/YGBvT5mOHbF8vRPG7u55dpWepaXn0iPciEoh6LO2MslLz49Wp+Wrk1+x6tIqXnV9lenNp6NRPz5ccjkikfYLDjKsokLf89tI3rMHQ2dnyo4bR0ZTX/ZdiWLPpUgOXIsmNVOLiaGa5lXtaF3DgZbVyz6YnZOl1bHuZCjzAq4RlZSBfw0Hxrd1p6pD3je3cpSVDlvGwPk14NEZOi8CI7On/SuRSqOUaPimAdhVhTd36B8ABOI3/EH4Rx9h1bsXjh9//GCcPS9CCD5cf57fToWxbEB92tR0lEleer6EEKwIWsGcU3Oo51CP+S3nY6l5fOz90y1B/HQ4hM0jm2N47iRJc77C/O4tzttVYmmt10l1qUyrGvr6N00q22JsmPs/iNTMbH48FMKSv66TkplN93rOvOtfDScrk8IGD4cXQsBUcKipH6e3qpB/O0l62Ia39LWZ3j6gX+cYSD11iluD3sS0fj1cvvsOxbBgM7p+PnqLyRsvMPqVKrzXxl3eeJVKju03tjPp0CQqWlRksf9iypmXe2R7QloWreb8RUJaFllagUqnZUjCeTqc3IxhajJW3btTdsxoDOzsCnzMuJRMvt0XzMojt1AUGNTUlREtKmNlalS44K/thvWD9Y+cv/GLfkqnJBXE9b3wcxfw/RBemQRAZtgdQnr0QF2mDK7r1qK2zGPCwUNO3Yql13dHaV7FjuUDG6BSKTLJSyXL8fDjjNk3BlMDUxb5L8Ld5tGxxz2XIll/Kgyfqva0qlEWhzLGaBMSiF60mNhVq1BpNNj9bwTW/fujMip4og6LS+Xr3dfYcCYMC40B/2tZhUFNXTE2VCN0OkRGBiqTfK7yo67C6l76GiLt50C9gU/yVyCVJpmpsLgJqAzg7UNgaIw2OYVbvXuTFRmJ65o1aCq5Faire4nptF94EFMjNZtHNsfSVH/lL5O8VOJcjbvKiIARpGSlMK/lPBqXa1ygdhk3bnJv1iySAwMxdHHBYfyHmL/yyiN1cfJzOSKRuVvOE370FI1Swmiti8Qq5Aq6tDRsBw3E7u23UZnlMe6eFqe/or++Fxq+pX/UXG1Q4ONLpczuqfpnLwZtA9fmCK2WsHdGkbx/PxW+W4p5s2YF6iYzW0fvZUe5eDeRjSOb4e747z0mmeSlEikiJYIRASMISQzh82af06FShwK3TT5wkMiZM8m8fh3TJo1xmDARY/dque6fHRVF6ukzpJ0+RerpM/rFTbL1JVxDLBwIK1+NmnYarA8GoLWxI2nQCNJ9W6FSqVAAlaKgKPrJNSpFQdFlU/HMLByDlpNYrhmG7T7HxMIWNGX0PzLpSwARf8NSP6jdB17X12O6N3s2Md8vx+Hjj7Hp17fAXU3eeIGfj97imz516ODl9Mg2meSlEisxM5F3973LiYgTvFv3XQbXGlzgq3KRlUXcmrVEffMNuqQkrHr2wH70aNRWVmQEB5N2+gxpZ06TevoMWaGhACgaDSaenpjUrYtJ3TqYeHuz524GX+64zPWoFKrH3mLE+T+oFh/G37aVWOTVmRBLp1xj6KH+i2kGP6BR/lPz29AMjO8n/BxfLfWvlhWgWtsHMy2kl4hOC9/7Q0Kofk68qQ3xf2wkfOJE/UyaKVMK/P/6bydDGbf+PMN9K/HRazUe2y6TvFSiZWozmXRwEjtCdtDLvRcTGk5Arcp/Gtk/suPiiP52EXGrV+vH1FUqdImJAKhtbTGtWweTOnUxrVcX4xo1UHIYx8/W6rgamYxWJxDabNixFYPliyE5iewOXcnoPwSdmQUC0AmBEPpJNzohyIoKZve+vSgZibzhZUlte5X+IaqMhPuviY+/Zqf/e/Bm70LrT5/2r1EqaY4ugR3jodty8OxO6ukz3B44EJNCzqQ5HxZP9yVHaOBqzYo3G2KgfvyCQCZ5qcTTCR1zT85lxcUVtHJpxUyfmRgbGBeqj4zgYKKXfofK2BiTunUxrVsHQxeXQo3XP0wbH0/UgoXErVmD2tKSsu+/h2XXrig5XHVHJ2cw6tczHLkRQ7/GLkzu4IHGII8PquxMfbLfN11fNva12dBw2BPFKZVA8aH6OvEVm0Lf38i6e5ebPXqisjDHbe1a1FZWBeomJjmDjgsPoigKW0Y1x8Ys54kGMslLL4xfLv7Clye+pIZtDcbVH0d9xxz/v32m0i9dIuLzaaSdPo2xpyeOkz/GxMvrsf2ytTq+2nWFpYE3qF3BisX96lLOMp/ZOtps/ao/13bqp2VWb19MZyE9M0LA6t5wMxD+dxSdkR0hffqSdfcurmvXoKlUqUDdZGt1DPjhOCdvxfH7203xdM59iqVcNER6YfTz6MfXLb7mXuo93tz5JsN3Ded81PnnGpNxjRpUXPULTl/OIisinJCeb3D344/Jjo19ZD8DtYqJ7WqwuG9drkUm0WHBQQ4H57Mos9oAui8HpzqwfgiEyYucF96lzXD1T2j5EcKyAnc+HE9GcDDlv/66wAkeYNaOyxy+HsOMLp55Jvj8yCQvlTitKrZie9ftfFD/Ay7HXqbv9r6M2jOKy7GX829cTBRFwbJTJyr/+Sc2gweTsHET19u2I/aXVYjsR2+6tvMsx6Z3mmNtZkS/5cdYEng9x7VuHzAyg95r9UXUfu2pr1IovZjS4mH7h+DoBY1GEPX1PJL37MFhwgTMmxdsqiTA5nN3WXbgJgOaVKR7Pec8903NSs1zuxyukUq0lKwUVl1axU9BP5GUmUTriq0ZWXskla0qF9kx7iTfYX/Yfq7EXsHCyAIrjZX+x1j/aq2xxlJjiaXGEgOVfmpkxvXrRE6fTsrhI2iqVcNp5hcYe3g80m9yRjbj159n29/htK3pyFc9vLAwzuNmW8x1/WwMY0sYGgBmBX+qVyoBkiLgzw/1i3IP20vCidvcHT8BqzfewPGTqQW+N3QpPJGuiw5T06kMvw5rjJFB7tfi1+Ku8X7g+2zpskWOyUsvtsTMRFYGreTniz+Tlp1G+0rtGeE9ApcyLoXuK1uXzfmo8wSGBbI/bD/B8cEAWGmsSM1KJVOXmWtbCyMLrDXW9z8ILPEMSqXRmr8xVhtTdctWDKwfXfRECMH3B24yc8dlKtqasrRfvbwLpYWegBUd9DVyBm4FI9Pc95WePyH0Q2zHlsDFjfppky0mkmrZhtsDBmJSty4u3y8r8EyahNQsOn5zkPQsLVtHN6esRc6TD4QQbAzeyIxjMzAzNCOwV6BM8tLLIS49jh8v/Mjqy6vJ0mXRuUpnhnsNx8k897nsoK9tf+jOIQLDAjl09xAJGQkYKAbUc6iHj7MPfs5+uFq6IoQgLTuNhIwE4jLiiM+I1/85Pe6R9+LT4/WvGfGY34zi0x/TMfFtTuXF3+V4xXbkegyjVp8mNVPLV929ae9VLoco77u0VX8z1r2d/mZsIaaTSs9IdgYEbdQn97un9c8/1OkPDYeSlW6sn0ljZobr2jWPffDnRqsTDFlxgkPB0awZ3uTxVdLuS81KZdrRaWy5sYVG5Rox02cm9qb2MslLL5fotGi+//t71l1Zh0DQvWp3hnkNo6ypfnEFIQQ3E24SGBZIYFggZ++dRSu0WGus8XH2wdfZl6ZOTbEweoLyw/9xI+EGP37clT67Myg7dTK2vfvkuF9EQjr/W3WK07fjGebjxvi21XOc8wzAse/gz3H6RcVfmy3r2JcUSRH6Ka8nf4CUKLCrpl+I27s3aMzRpaQQ0rcfWWFh+pk0lQs+rDhn1xUW7g1mWuda9GtcMcd9/hmeCUkIYUTtEQz3HI5apS6+KZSKonwOvA7ogHvAICHEXUV/KTMfeA1Ivf/+6fz6k0leKqyIlAiWnl/KxmsbUavU9KjWA53QERgWyJ3kOwC4W7vj6+yLXwU/atnWKtSDVgW1NXgzSaMm4HlHTdU/Nub6jzszW8e0bRdZeeQWjdxs+KZP3Qd18B+zazIcXgD+n0Lzd4s8ZqmAchqSqfYqNHoLKrVEAJk3b5Jy6DAJW7eQ/vcFKixdgrmPT75dhyekEXAxkl0X9esk9KzvzKxuXo99G/zv8Mws31k0KtfowfbiTPJlhBCJ9/88GvAQQrytKMprwCj0Sb4RMF8I0SiPrgCZ5KUnF5oUypJzS9h6YyuGKkMal2uMr7Mvvs6+OJo5PpMYZm4fT8tJmzF1cqbmH9vyrJC54XQYH/3xN5YmhqwZ3gQ3uxwKoul0sGGovgZ51+/Bq0cxRi89Jo8hmWxdGVKOHCHl8BFSjhwhOyICAEMXF+xGjMCqS+ccuxRCcDUymd0XI9h1MZLzYQkAVLIz4zXPcrzzSpXH1kjIaXjGzuTRm/LP5GEoRVEmAi5CiBGKoiwF/hJCrL6/7QrQQggRnlcfMslLTysmLQZTQ1NMDAq5MEgRSM9O57PZr9P/p9to+vag0uTP8tz/4t1E+i0/hqWJIRtGNMU6p6cZszPg564Qegz6bwA332KKXgL0V+3RV/UfrA8Nyei83yQ1szIpJ86ScvgwGVevAqC2tMS0SRPMmjbBrGlTjJwfn+6o1QlO3Yp7kNhvxeinPNZxsaK1hwNtPBypUtY8x3ByG575r2JN8oqiTAcGAAlASyFElKIoW4GZQoiD9/fZA4wXQuSZwWWSl150IQkhbBnZCf+TWZT7bjFWvi3y3P9kSCx9vj+Gt7MlPw9plPNKV2lx8ENb/Zqzg3eAg8fj+0hPLiMJbgRCcAAE74GE2widQrq5DymZ1Um5Eknq2bOQlYViZIRJvbqYNW2KWZOmGHvUyLHMRXqWlgPXotl9MYI9l+4Rk5KJkVpF0yq2tPZwoHUNB8qWyb1sR37DM//1VEleUZQAIKfvu5OEEJse2m8iYCyEmKooyjbgi/8k+Q+FEKdy6H84MBzAxcWl3q1bt/KMR5JKuh2XN6EMnYBDlgm1tu/GwNY2z/23nLvLqNVn6OTtxLw3aqNS5XCTNT5UP4deZQBDd0OZvGcTSXkQAiKD7if1ALh9BHTZYGSOcPMj7ro1UZuOo0tKBkDjUQPzpk0xbdIE03r1UBnnnpz3Xo5k7YlQ9l+NJi1Li4WxAa9UL0trDwf8qtnn/ZzEfQUZnvmvZzVcUxHYJoSoJYdrpNJu/rr3afHpdsT/27vv+JyuP4Djn5PkiSzZiRAkQszYo6r2plpFq0tVKa29S4cW/RU1q2arqFFdVI2WGrFqhipBImKHDDJFZD7n98d9KPJkkZ3zfr3yypN7zz33nDzP/ebm3DMa1abu9z9nORBm0Z5gZv11nmFtqjCuUzXjiUJPw8ou4OAJ72zTpipWsudeDFza89/d+h1DKCrjA1XaQ5X2pNnXJPSzqdzZsQPrFi2w7/ESVk2bYubomGX2UkoW773IrL/O42ZrQcdaWjNMk0qOmQ5melx2m2cel1mQf6qVDYQQ3lLKC4YfXwTujzvfDAwTQvyE9uA1NqsAryjFyfu9prHwxEm6bvLn6ooleA4Ykmn6Ia0rcy0ygYV7gqnoZEXvRkYWCi9bB3qv1qY++OUteONXMMvhOrUlye0LWm+YC7sgxA9kmjaa2KvNg8COrTZeITEwkJDX3iAl5Aau48fj2P+dbI9QTU3T89nms/xw9Bo9bYFWBgAAIABJREFU67szo1edHAV2gBR9CpuDNzPj2AysddYs67gs0+aZnHja3jUbgGpoXSivAu9LKW8YulAuBDqjdaF8J6v2eFB38krxcjX2Koff7EatK2lUWr8em+qZt6WnpOnp/70fhy9Gsqp/E56rksG/6Cd/gE1DoEFfeHFBHpS8GIi8CEuaafP2l60H3h20oO7eKN2qXTEbNhA29XNM7exwnzcXq4YNs32ahORURvx4kl0BEQxpXZnxnapl+cchISWBoOggAqICCIwKJCAygOCYYFL0KdlunnmcmmpYUQrIzn/XYzNgEiYODjTe6ptpey5AXGIKLy85RGhsIr8NbpbxFAi7psDfc+HlleDTMw9KXoRJCWt7an3b39sPjsYXydbfu0fY1M+J3bgRq2eb4j57dpbPTx4WGZ9E/1XH8Q+JYUp3H94yMoAp8l6kFsijAjgfdZ7AqECuxl1FosVd+1L2VHesTg3HGvg4+9CuYrsnGsehgryiFKDvvh3Kc3N9udu9FY2+XJpl+pDoBHosPoS5qQkbhzYzPn9JWorW4+b2BRh8EOyNNO+UVGd+g/XvQJeZ2oAlI5IuX+bGyFEkXbiA8+D3cR46FGGa/eB6NfIub684RmhsIgter0/HWlrflONhxzl08xCBUYEERgVy696tB8e427hT3bE61RyrUcOxBtUdq1PGqswTL2rzMBXkFaUAJacls/b9djx74DaWX/0Pz869sjzmdEgMr35zhKplbPhp0LNYmhsJQFGXYGkLbVrbflvVHDegLa+4sDGUdoOBvkZ/J3HbtxP68ScInY5ys2Zh06J5jk7x7/UYBnzvh15Kvnu7MQ09HIi8F8mXfl+y7fI2TIUpXvZe1HCsQTWHatRwqkE1x2rYmufdg3IV5BWlgF2LvEhAzxdxijeh1h87sHTLZIIygx1nw3hv7Qk61izD4jcbYmqsa+Wpn2Dje9DmE2g1Pg9KXsRsmwBHv9ECvHuDR3bJ5GTCZ88mevUaLOvWxf2reejKZv0+PGx3QDjD1p3EubQ5q95pQiVna34P/p3Zx2dzL/UeA2sPpJ9Pv3wfjKdWhlKUAlbRqTLmn09Al5TKsWF9kHp9lsd0rOXGpOdr8tfZcKb/GWA8UZ1Xwedl2Dtdm6a4JLv5Lxz7VpvU7bEAn3LzJlff6kv06jU4vt0XjzWrcxzgfzx2jYGrj1PF1YbfBj+HaalI3t3xLp8e+pQq9lVY/8J6BtcbXCCjrTOjgryi5JM2LfsS0KcprmducnT+pGwd0795Jfo18+S7vy+z5vCV9AmEgG5zwdZdm+cmMS5Xy1xk6NNg62iwcoa2nzyyK/7AAS736KktwTd/PmU+/BCRybxCj5NSMnfHeT78zZ+WVV1Y+25Dfr+8mp6benIu8hyTmk5iZeeVeNlnf2m//KSCvKLko57jviGwli1W3/3GleN7s3XMpG41aV/Dlc82n8U3MDx9Ags76LUMYq5pKxOVRMdXaJOIdZ4OlvYAyLQ0IubP5/qg9zBzc6PShvXYduqYo2xT0vSMX3+ar32D6d2oPCO6mvPOzjf5+uTXtKrQik0vbaJ3td6YiMIbSgtvyRSlGDI3M6fhVyu4aym4NmYUifGxWR5jaiKY/1p9apS1Zdi6k5y5YeSYik2h5Xg49SP4r8+Dkhdid8Jh91Twag0+2kNtqddzffBgIpcsxa5nDzx//glzT88cZRuflMqAVcdZfyKEIW3L41DhT/pt70tsUizz28xnbuu5D9YvKMxUkFeUfFaxQi1SJw3DKSIJ3/FvZesY61JmrOjXGHtLHQNW+REaey99opYfQPkmsHUMRJegOaB2fKwNeuo658HiKnFbtnB3/wFcJ06g3BdfZDk+4XERcYm8+s1hDgbf5t2O99gZN44fA3/k1Wqvsqn7JtpWbJsXNckTKsgrSgFo0X0Il573odKeC5wY8hap0dFZHlPG1oIV7zTmblIa/b8/TnxS6qMJTM20Zhuph98GQVqq8YyKk4t7wP9XaD4GnKsAoE9KImL+fCxq1cKxb98cZ3ktUhuncCkqlGbP/snP16dgo7NhdZfVfNz0Y2zMjU8LXFipIK8oBaTTtNXs61AG873HCe7albg//ySrLs3V3WxZ/GYDgsLvMHCVkUDv4Kk9iL1+BA7MybOyFwopifDHWHD0guajH2yOXvcjqTdDcR031ug0wJlJ00tG/fIPcboD2HrP41zMYYbXH84v3X6hnmu93K5BvlBBXlEKiHkpS56ftoYpA0tzwyaZG2PGEjJ0GCnhEZke17KqC7NfqcOxK1G8uewIUXeTH01QpzfU7g37voTrx/KwBgXs4HyIuqitgavTmmPS4uKIXLoU6+bNsX722Rxn+f2hK5yJ3wTO66npVJ0NL25gUJ1B6EyzniK4sFJBXlEKUIXSFRjw0hRGv5FE0JvPcvfQIS5160b0r79melffo355vunTkMCwO7yy9BA3Yx5ro39+Nti5w4Zi2q0y8qL2n0qtnlCl3X+bly0jLS4O13Fjc5zlldt3mbXrKJYue2hdvjUrOq3A084zFwtdMFSQV5QC1qVSF3pU68WkiseJ+24yFjVqEDbpU66905/k69czPK59zTKs7t+EiLgkXl5yiOCI+P92Wthp68LGhsCf4/KhFvlISq1OZqWg07QHm1NCQ4lavQa7F1/Aonr1HGWp10s+2HAanfN2TE31fND4g1yZU6YwUEFeUQqBCY0nUMmuEhMufYX1ktm4TZlCor8/l17sTtSqVci0NKPHPePlxE/vNSU5TfLK0kOcuh7z386Kz0CrCXD6Zzj9Sz7VJB+c/Q0u+mqDnmz/G7V6a8FC0OtxGTEix1muPXqV46GnoPQJ+tTsQwXb4jPhmwryilIIWOmsmNVqFnFJcXxyeBJ2vV/G64+tWDdpQvj0GVx54w2SLlwwemytcnZsGPwsNhZmvL7sCH9fuP3fzhZjoUJTQ7fKK/lTmbyUGAvbP9LmiG/87n+bg4KI/f13HPr0QefunqMsr0clMGNbAK6e23GycGJQ7UG5XeoCpYK8ohQSVR2q8kHjDzh44yCrz65G5+ZG+aVLKDd7NinXrnOpZy9uLV6MTE5Od6yHkzUb3m9GRUcr+n/vx5/+hoXYTM2g57da//ENA4t+t0rfLyA+HLrNe2SGyVtz5mJiY4PzezkL0FJKJmw4jYnNv9wVFxnRYESR6yKZFRXkFaUQ6V2tN+0rtmf+P/Pxv+WPEAK7bs/j9cdWbDt25PbXC7j88ivc8/dPd6yrrQU/D3qWOuXtGLruH344ahgQ5eChBcWQY7B/Vj7XKBfdPAl+y9JNQHb32DHi9+3DedBATO3tc5TlumPXOHQpFFv3HdRwrEH3yt1zu9QFTgV5RSlEhBBMbjYZFysXxu8fz53kOwCYOTriPmc25RcvJi02liuvvsathYvS9cCxs9KxZsAztK7qwscbz7BoT7CWpvbLUOc12D8Trh0piKo9nfsTkFm7QLv/JneTUhIxew5mbm449OmToyxvxNxj+p+BVPb2407qbSY0mfBEqzIVdirIK0ohY1fKjpktZxJ2N4yph6c+EshLt22D19Yt2L3QjdsLFxI+bXq6aYstzU35tm8jetR3Z9Zf5/l8awB6vYSus8C+otZsE3kxv6v1dI6v0O7kO03Teg4Z3PnrLxJPn8ZlxIgcTV0gpWTihtPoTaKJ1e2go0dHGpbJ/tquRYkK8opSCNVzrcfQekPZfmU7G4M3PrLPtHRpys6YgWO/fkSvWUPop5+m632jMzVhzit16dfMkxUHLzP211Ok6Gyg13K4ewsWNoL1/SEsfbNPoWNkAjIAmZJCxLx5lKpaFbvuL+Yoy1+Ph3Dgwm18ah9AIhnTaEzulrkQMcs6iaIoBaG/T3+Ohh1l+tHp1HWpS2X7yg/2CSFwnfABJlaW3F68BJmYRLnp0xC6/0ZmmpgIPnuhJk7W5szZGUTsvRQWvdEAy5Gn4Mgi8FsOZzaAdydoMUabybIw+uujdBOQAUT/8gspV69R4ZulOVqfNSw2kc//OEdtr2gC7uxnYO2BuNvkrEdOUaLu5BWlkDI1MWV68+lY6awYt28ciamJj+wXQuAyYgQuY8cQt3UrN8aMQf9YzxshBMPbefO/l3zYcz6CviuOEmvmCB2mwugz2rKBIX6wohOs7ArBu7TBRoVBbAj8PQ/OrH9kAjKAtPi73F60GKsmTbBu2TLbWUop+WijPylpqehct+Bi6cK7td/N+sAiTAV5RSnEXKxc+KL5FwTHBDPLz3jPGOeBAynz8cfc2bmLkKHD0N9LPw1xn6YeLHi9Pv9ej+HVbw7zs981zkWbktp8rBbsO8+AqMuwthd82wrO/q497MxPej3c+Af2TIOlzWFeLdg1WZs++aEJyACiVqwgLSoK1/HjcjQydePJG/gGRvB8szAuxJ5jVMNRWOmscrkihYtayFtRioC5x+ey8uxK5raeSwePDkbTxKxfT+ikT7Fq3JjyixdjamOdLs2BC7cY9dO/RBomNStlZkKtcrbUKW9P3XKWNLvri+upxYioi+DkDc1HaZOdmWV/ubwcSbkHl/fD+W0QtB3uhIIwgQrPQNXOUK0rOHs/0kyTeusWwZ06Y9OqJeXnzcv2qSLiEukwbz9ermbEOv0PN2s31nZdW6hXdcquzBbyVkFeUYqAlLQU+m7ry9W4q/z64q8ZtiHHbv2DmxMmYOnjQ4Vl32Jqa5sujV4vuRqVwOmQGE6HxOIfEsuZm7EkJGt37ralTHjH4TSvJ6/HLSGIVJtymDYfiWjQF8xz4a43PgKC/tKC+kVfSEkAnbU20Vi1LuDdEaydAVj+92WW7b/Eq40r0KepBy6lSxE6eTIx6zdQ+Y+tmHt4ZOuUUkreW3OCfUG3eLPzOX69+D1ruqwpstMHP04FeUUpBq7fuU7vLb2pbF+ZlZ1XojMxPv1t3M6d3BgzllLeVai4fDlmDg5Z5p2ml1y8Fc/pkNgHwf9caCzN9CcZbLaZZ0wCiTOxI7JiJyqVddUmBzMtpd3hP/K9FJiaP/rdzEJr57+yH85v154BILXFx6t1gapdwLP5g+mC77uTmMJzM3wxNzPhdnwy5mYmvO0u6LlwHA6vvYbbpE+MV8aIzaduMuLHkwxt78BPocNp79GeL1t+me3jCzsV5BWlmNh+eTvj94/n3drvMrLByAzTxR84QMiw4egqlKfiihXoXHO+Fmlyqp6g8DucDonlTtA+6lxZSY3UQGx1ekzSkkFmr81enwa3/G2xsE/BtklVRM3noVpncKvzSDPM4xbvDWbm9vNsHvYcVuZmrDx4mcoLP6dueBDL3pvNG53q0sLbOcs2+dvxSXSYuw8PJ2u8av3GgZD9bOmxBTdrtxz9PgqzzIK86kKpKEVI50qdORJ6hOX+y2ns1phm5ZoZTWfTogUVvv2W64MHc/Wtt/BYuRJduXI5Ope5mQk+7nb4uNvBM28REfcKz8zcw/O1yjK3dz0teqcmQVoSpCYbviel2xa5dgtRgX8CcOuaDsd+5bBv4o1JJsE5ITmV7w5cplVVF+qU16Yq+Ngzlas3/LnY7Q1OxMH2FceoWsaGAc0r0b2eOxY6490oP9t0lrtJafRrm8akYzsYUndIsQrwWSn6TxwUpYSZ0GQCXnZejNs3jvNR5zNMZ/1MEzxWLCctKporffqQfPXpFvd2tbWg77Me/H7yhjZ3vYmp1kZv6QCly2ijaZ29wc0H3BuCx7Mk4UHkrzsp3aUz5RcvxqxsWcKnTSO4TVtuff01qZGRRs+17ug1ou4mM6Kd1m3y/vQFpi7OdJ06lr8ntGHOK3UxEYIJG/x5boYvX+0K4nZ80iP5bPMP5Q//UIa382Jd8ELcrN3o59PvqX4PRY0K8opSxFiaWbKo/SIszSx5b+d7XI3LOHhb1quHx6rvkfcSudrnLZKCg5/q3O+3qoyFzpT5u41Pe/wwKSVhn32GsLTE7aOPKN22DZ4/rMVj3TosGzfi9uIlBLdtR+iUKY/8AUpMSePb/Zd41suJhh6OAMTv2cO9EydwGToMEysrSpmZ0qthebaNbMG6d5+hbgV7vtp1gWYzfJm44TQXwu8QdTeZSZvOUNvdDjd3fwKiAhjdYDSWZpZP9TsoalSbvKIUUZdiLtFvez8szCxY3WV1pk0QScHBXH3nHUhNo+KK5VjUqPHE5/1yeyBL911k+8iWVHMrnWG6mA0bCP34E9ymTsGhd+/0Zbp0iaiVK4n9fRMyLY3SHTviNKA/v8aX5tNNZ1k38BmaVXZGpqZyqftLoNfjtWUzwsx4K3NwRDwrDl5mw4kQklL1lLWz4HZ8Ej8Prs/og69RsXRFVndZXWxWfHpYZm3y6k5eUYooL3svlnZYyp3kOwzcMZDIe8abPgBKVamC55o1CAsLrr7dj8TAwCc+76AWXlibm/HVrqAM06RGRhI+cxaWjRpi//LLxsvk5UXZzz+n8u5dOA0YwN2DB7nySm9sPhzJazKEppW0u/iYjRtJvngRlzGjMwzwAFVcbZjWozaHP2zH2A5VMRGCDzpVZ2/4j0QlRjGhyYRiGeCzkitBXggxTgghhRDOhp+FEOJrIUSwEOK0EKJBVnkoipJzNZ1qsrDdQsLuhvH+rveJS8540W5zT088167BxMKCkKHDSI2OfqJzOlib0795JbadCePszVijacKnz0CfkEDZKVMQJpmHGZ2rK65jx1Bljy83Xx+IS0w4b2/6iisv9SDmt43cXrAQy/r1Kd2+fbbK52htzvB23hyc2JbO9XSsPbeWFyu/iI+zT47rWhw8dZAXQlQAOgDXHtrcBfA2fA0CljzteRRFMa5hmYbMazOP4Jhghu0exr3U9NMa3Kdzd6f8gq9JjYjg5tixyNQnWylqQPNK2FqYMW9n+rv5+AMHiNu6FedBgyhVubKRo42TllZMsazP3HdmUHb6NKQ+jdCPPiI1IiLH0xfcN/v4bMxMzDLtblrc5cad/DzgA+Dhxv3uwGqpOQLYCyHKGj1aUZSn1ty9OTNazODUrVOM3jOa5LT0SwTeZ1m3Lm6TP+PuocNEzM3+tAAPs7PUMbCFF7sCIh5ZPFyfkEDY5CmYV6qEUw6X4tt86ibXohIY3L469j164LV5M+WXLqHstGlYNch5Y8DR0KP4XvdlYO2BuFrlfJxAcfFU/eSFEC8CN6SUpx77K+sOXH/o5xDDttCnOZ+iKBnr5NmJhJQEPj30KRMPTGRmy5mYmRi/xO179SLx7FmiVqzAomZN7Lo9n+PzvdO8EisOXmbuziBW9W8CwK2Fi0i5cQOPNasxMc/+fDdpesnCPcFUdytNh5plABAmJqQ0rYPvNV+SAn4gOS1Z+9Ink5KWQlJaEsl6bVtKWsqD1/f3X4m7gruNO31r9c1x3YqTLIO8EGIXYOyx/cfAR0BHY4cZ2Wa0G48QYhBakw4VK1bMqjiKomSih3cP7iTfYdbxWUw5PIUpzaZkOAFXmYkTSTwfROgnn1CqsleOe9zYlDLjvVaVmbEtkBNXo6h1N4yoVauwf+VlrBo3zlFe286EcunWXRa90eBBs0xCSgLv7niXC9GPdtc0ESaYm5ijM9VhbmKOuan2pTPRaa8N23ycfRhYeyClTEvlqCzFzRN3oRRC1AZ2AwmGTeWBm0ATYAqwV0r5oyHteaC1lDLTO3nVhVJRcseifxex9NRS+tTowweNP8iwPTv19m0u93oZYWaG5/pfszXPzcMSklNpOXMP1Vys+d+OuaSEh1P5j62Y2tllfbCBXi/p+vUBUtL07BjdClMTgZSSCfsnsP3Kdua1mUdD14YPgnlG/52UZHnShVJK6S+ldJVSekopPdGaZBpIKcOAzUBfQy+bpkBsVgFeUZTcM6TuEPrU6MPagLUsPbU0w3Rmzs7ag9hbt7gxZkyOH8RamZvxfqvKuOzcROLZs7h99GGOAjzAroBwAsPuMKxtFUxNtD9Gq8+tZtuVbYxoMIJ2Fdthb2GPlc5KBfgnkFf95P8ELgHBwDJgSB6dR1EUI4QQjG88nu6Vu7P41GLWnFuTYVrLOnVw++wzEg4fIWLO3Byf6zUPc94O3E6QZ21sOnfO0bFSShb4BuPhZMULdbS5dY6EHmHuCW3e/AE+A3JcHuVRufZn0XA3f/+1BIbmVt6KouSciTBhcrPJJKQmMNNvJjY6G3p49zCa1r5XT+1B7MqV2oPYF7pl6xxSSqK/+B86E8EX3i/geDGSFt4u2S7j3qBb+N+I5ctetTEzNeFm/E3G7xtPJdtKfP7c5yVy8FJuUyNeFaUYMzMxY0aLGTQr14zJhyez48qODNOW+XAilo0aEjppEokBAdnK/85ffxG/bx8uI0dgVq4cc3YEkd3nfFJKFuy+gLu9JT3qlycxNZFRe0aRpk9jftv5WOvSr2yl5JwK8opSzJmbmjOv9TzqutRlwoEJ/H3jb6PphE5H+a++wtTePlsjYtPi4gj74gssatbE9e2+DG/nzb/XY9h7/la2ynX4YiT/XIvh/VZe6EwFUw9PJSAqgOktpuNhm70Vn5SsqSCvKCWAlc6Khe0WUsW+CqP3jMYvzM9ougcPYm/f5sbozB/ERsyZS1pkFG5TpyLMzHi5YXkqOFoyd2f27ua/9r2Aa+lSvNKoAusC17Hl0haG1BtCqwqtnrieSnoqyCtKCWFrbsvS9ktxt3Fn6O6hHAs9ZjSdZe3auE2eTMKRI0TMnmM0TcKJE8T8/DOOffti6VMLAJ2pCSPaeuN/I5Yd58IzLYvflSiOXIpiUEsvzkSeZJbfLFpXaM17dd57ukoq6aggrygliJOlE8s7LX8Q6I+EHjGazr5nDxzefJOo778ndsvWR/bpk5MJ/fQzdOXK4TJ82CP7etR3x8vZmnk7g9DrM76bX+AbjJO1Oe1rWzB231gqlK7AtObTMhy4pTw59RtVlBLmfqCvYFuBYbuHcejmIaPpykycgFWjRoR+8gmJ58492B753XckX7yI22efYmL96MNRM1MTRrb3JjDsDtvOhBnN99T1GPYH3aJf8/J8dHAciamJzG8zn9LmGc9Nrzw5FeQVpQRytHBkecfleNh6MHz3cA7eOJgujdDpcJ//FaYODoQMG05qdDRJly4TuWQptl27YNPKeNt5tzrl8Ha1Yd6uINKM3M0v8A3G1tKMULN1+N/2Z1rzaXjZe+V6HRWNCvKKUkI5WDiwvONyvOy9GOE7ggMhB9KlMXNyovyCBQ8exN5fzq/Mhx9mmK+piWBU+6oER8Sz5dTNR/aduxnHroBwmtW7wJZLvzOw9kDaebTL9bop/1FBXlFKMHsLe77r+B2V7Sszcs9I9ofsT5fGsrYPblOmkHDkCAl+friOG4uZS+YDnrr4uFHdrTTzd18gNU3/YPuiPcHY2IZwNHY5zd2bM7SeGjOZ11SQV5QSzq6UHcs6LsPbwZuRe0ay9/redGnse7yE8/Bh2L74QobL+T3MxEQwpkNVLt++y28nbwAQHHGHbQHnsaqwlrI2ZZnRYgamJqa5XR3lMSrIK4ryINBXd6jO6L2j8b3mmy6Ny9ChuM+cmeVyfvd1qFmG2u52fL37Ailpehb4nseq/A9IoT1otSuVs4nMlCejgryiKIDWj/7bjt9S07EmY/eOZffV3U+VnxDa3XxI9D1m7zjPX2HfICyv8nnzz/F28M6lUitZUUFeUZQHSpuX5psO31DLuRbj9o1j59WdT5Vf62ou1K9oz/J/f0HncITe3m/R2TNnM1UqT0cFeUVRHmFjbsPS9kvxcfZh/L7xbL+yPcd5RCdGs/PqTqYfm85d5xlYlluPq5kPHzYdkwclVjKjZuBXFCUdG3MblnZYypBdQ5i4fyJSSrpU6pJh+pjEGI6HH8cvzA+/cL8HS/ZZmllS36U+jZzbM6JxX7XoRwFQv3FFUYyy1lmzpP0ShuwewsQDE9FLPc97aQt+xybFcjzsOH7hfviF+REUHQRoQb2eSz261O9CY7fG1HKuhc5EV5DVKPGeeI3XvKDWeFWUwichJYFhvsM4EX6Cbl7dOB91nqDoICQSC1ML6rnWo7FbYxq7NcbHyQedqQrq+S2zNV5VkFcUJUv3Uu8xdu9Y/ML8qOtal8ZlDEHd2QdzU/OCLl6Jl1mQV801iqJkydLMksXtF6OXejVTZBGj3i1FUbJNBfiiR71jiqIoxZgK8oqiKMWYCvKKoijFmAryiqIoxZgK8oqiKMWYCvKKoijFmAryiqIoxVihGvEqhLgDnM+DrO2A2EKY18Ocgdu5mF9elTMv8s3tukPRqX9e1B3ypv5F5b2H3C9rYa97NSllaaN7pJSF5gs4nkf5flsY88rLuudhOXM937x434tK/YvCZ76ovfd59D4V6rpnlldJaa7ZUkjzykt5VU5V/6IhL8pZVOoOuV/WolT3RxS25prjMoNJdoo7VXdV95KoJNc/N+ueWV6F7U7+24IuQAFSdS+ZSnLdoWTXPzfrnmFehepOXlEURcldhe1OXlEURclFKsgriqIUYwUS5IUQ8QVx3oImhOghhJBCiOoFXZaCktV7L4TYK4QoVg/ihBDlhRCbhBAXhBAXhRDzhRAZLqckhBglhLDKzzLmtZJ6zUPBX/fqTj5/vQ78DbyWk4OEEKZ5UxwlrwkhBPAb8LuU0huoCtgAX2Ry2CigWAX5Eq5Ar/sCC/JCCBshxG4hxD9CCH8hRHfDdk8hRIAQYpkQ4qwQYocQwrKgyplbhBA2wHPAAAxvthCitRBivxBioxDinBBiqRDa0jtCiHghxFQhxFHg2YIree4z1HvrQz8vFEL0K8Ai5aW2QKKUciWAlDINGA30F0JYCyFmGz7/p4UQw4UQI4BywB4hxJ4CLHeuK2nXPBSO674g7+QTgR5SygZAG2CO4a4HwBtYJKWsBcQAvQqojLnpJWC7lDIIiBJCNDBsbwKMBWoDlYGehu3WwBkp5TMy6I+8AAAEpklEQVRSyr/zvbRKbqkFnHh4g5QyDrgGvAtUAupLKesAP0gpvwZuAm2klG3yu7B5rKRd81AIrvuCDPICmCaEOA3sAtyBMoZ9l6WU/xpenwA88794ue514CfD658MPwMck1JeMtzh/Qg0N2xPAzbkbxGVPCAAY/2UBdASWCqlTAWQUkblZ8EKQEm75qEQXPdmuZlZDr0JuAANpZQpQogrgIVhX9JD6dKAIv2vmxDCCe3fdh8hhARM0S78P0kfAO7/nGj4ABRHqTx6g2GRUcJi4CyP3ZUKIWyBCsAljP8BKK5KzDUPhee6L8g7eTsgwvBmtwE8CrAsee1lYLWU0kNK6SmlrABcRvvr3UQIUcnQJvcq2gOa4u4qUFMIUUoIYQe0K+gC5aHdgJUQoi88eJg2B/ge2AG8L4QwM+xzNBxzBzA+o2DRVpKueSgk132+B3nDBzoJ+AFoJIQ4jvYXPjC/y5KPXgc2PrZtA/AGcBiYAZxB+wA8nq7YuP/eSymvA78Ap9E+BycLtGB5SGpDynsArwghLgBBaG3THwHfobXNnxZCnEL7PIA2RH1bcXnwWkKveSgk132+T2sghKgLLJNSNsnXExdCQojWwDgpZbeCLkt+UO99yaTe90fl93Wfr3fyQoj30R4yfJKf51UKnnrvSyb1vhc8NUGZoihKMZand/JCiApCiD2GgQ5nhRAjDdsdhRA7DcO8dwohHAzbqwshDgshkoQQ4x7La6QQ4owhn1F5WW5FUZ7ME1zzbxoGgp0WQhwyNO3cz6uzEOK8ECJYCDGxoOpU1OXpnbwQoixQVkr5jxCiNFr/15eAfkCUlHKG4c1zkFJOEEK4oj1xfwmIllLONuTjg9bHtAmQDGwHBkspL+RZ4RVFybEnuOabAQFSymghRBdgspTyGUMvpCCgAxAC+AGvSynPFUS9irI8vZOXUoZKKf8xvL4DBKANgOgOrDIkW4X2IUBKGSGl9ANSHsuqBnBESplgGDiyD63HgqIohcgTXPOHpJTRhu1HgPKG102AYMOAoWS0m7zu+VOL4iXfHrwKITyB+sBRoIyUMhS0DwXgmsXhZ4CWQggnoc3O1xVtMImiKIXUE1zzA4BthtfuwPWH9oUYtik5lC8jXg2T9GwARkkp4/6briJ7pJQBQogvgZ1APHAKbdSkoiiFUE6vecPgqAH8N7zf2AGql8gTyPM7eSGEDu3N/kFK+Zthc7ih7e5+G15EVvlIKZdLKRtIKVsCUYBqj1eUQiin17wQog7awLDuUspIw+YQHv1vvTzaxG1KDuV17xoBLEd7sDL3oV2bgbcNr98GNmUjL1fD94poM7b9mLulVRTlaeX0mjdcz78BbxlmarzPD/A2DP03R5umd3Nel784yuveNc2BA4A/oDds/gitje4XoCLasO5XpJRRQgg34Dhga0gfD9Q0/Lt3AHBCeyg7Rkq5O88KrijKE3mCa/47tAncrhrSpkopGxny6gp8hTax1wopZWYLrSgZUIOhFEVRijG1/J+iKEoxpoK8oihKMaaCvKIoSjGmgryiKEoxpoK8oihKMaaCvKIoSjGmgryiKEoxpoK8oihKMfZ/OI0pw7DZ2CUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以21天为周期重采样 \n",
    "df_stock0_21 = df_stock0.cumsum().resample('21D').ohlc()\n",
    "df_stock0_21.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../../abu/'))\n",
    "from abupy import ABuMarketDrawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_stock0_5['open'].values属性， values 是一个Numpy对象 ； DataFrame\n",
    "ABuMarketDrawing.plot_candle_stick(df_stock0_5.index, \n",
    "                                   df_stock0_5['open'].values,\n",
    "                                  df_stock0_5['high'].values,\n",
    "                                  df_stock0_5['low'].values,\n",
    "                                  df_stock0_5['close'].values,\n",
    "                                  np.random.random(len(df_stock0_5)),\n",
    "                                  None, 'stock',day_sum=False,\n",
    "                                  html_bk=False, save=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
